Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis

doi:10.3748/wjg.v32.i11.116220

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Mar 21, 2026; 32(11): 116220
Published online Mar 21, 2026. doi: 10.3748/wjg.v32.i11.116220

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Figure 1 Flowchart of patient selection and dataset allocation. A total of 823 patients were initially screened from three centers. After applying exclusion criteria, 211 patients from center 1 were allocated into the training (n = 150) and validation (n = 61) sets, while 100 patients from centers 2 and 3 were used as the independent test set. AC: Acute cholecystitis; CT: Computed tomography; PC: Percutaneous cholecystostomy; LC: Laparoscopic cholecystectomy; ASC: Acute suppurative cholecystitis.

Citation: Chen GD, Chen BQ, Ge YH, Liu JL, Cheng KW, Xiao HW, Long HY, Xie F. Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis. World J Gastroenterol 2026; 32(11): 116220
URL: https://www.wjgnet.com/1007-9327/full/v32/i11/116220.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i11.116220

Chen GD, Chen BQ, Ge YH, Liu JL, Cheng KW, Xiao HW, Long HY, Xie F. Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis. World J Gastroenterol 2026; 32(11): 116220 [DOI: 10.3748/wjg.v32.i11.116220]

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